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# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from argparse import ArgumentParser
import torch
from lightning.pytorch.loggers import TensorBoardLogger
from megatron.core.dist_checkpointing.validation import StrictHandling
from megatron.core.distributed import DistributedDataParallelConfig
from megatron.core.optimizer import OptimizerConfig
from nemo import lightning as nl
from nemo.collections import llm
from nemo.collections.llm.gpt.data import ChatDataModule, MockDataModule
from nemo.collections.nlp.modules.common.tokenizer_utils import get_tokenizer
from nemo.lightning.pytorch.callbacks import ModelCheckpoint
from nemo.lightning.pytorch.optim import CosineAnnealingScheduler
from nemo.utils import logging
# Suppress lengthy HF warning
os.environ["TOKENIZERS_PARALLELISM"] = "false"
def get_args():
"""Parse the command-line arguments."""
parser = ArgumentParser(
description="""
Script for training GPT models. Supports 4 modes, with different arguments needed in addition to the required arguments:
1. Pretrain: no additional arguments required
2. SFT: --use-chat-data required
3. Distillation: --teacher_path required
4. SFT Distillation: --use-chat-data and --teacher_path required
"""
)
parser.add_argument("--name", type=str, required=True, help="Experiment name")
parser.add_argument(
"--model_path",
type=str,
required=True,
help="Path to NeMo 2 checkpoint. If only model_path is provided, the model will be trained (pretrain or SFT). If teacher_path is also provided, the model will be distilled.",
)
parser.add_argument(
"--teacher_path",
type=str,
required=False,
help="Path to NeMo 2 checkpoint to use as a distillation teacher. Will trigger distillation mode if provided.",
)
parser.add_argument("--kd_config", type=str, help="""Path to Knowledge-Distillation config file""")
parser.add_argument("--tp_size", type=int, default=1, help="Tensor parallel size")
parser.add_argument("--cp_size", type=int, default=1, help="Context parallel size")
parser.add_argument("--pp_size", type=int, default=1, help="Pipeline parallel size")
parser.add_argument("--ep_size", type=int, default=1, help="Expert parallel size")
parser.add_argument("--precision", type=str, default="bf16-mixed", help="Datatype for models and optimizer")
parser.add_argument("--devices", type=int, default=1, help="Number of GPUs to use per node")
parser.add_argument("--num_nodes", type=int, default=1, help="Number of nodes to use")
parser.add_argument("--log_dir", type=str, required=True, help="Folder for logging and checkpoint saving")
parser.add_argument("--max_steps", type=int, required=True, help="Number of global batches to process")
parser.add_argument("--gbs", type=int, required=True, help="Global Batch Size")
parser.add_argument("--mbs", type=int, required=True, help="Micro-batch Size")
parser.add_argument(
"--data_paths",
nargs="+",
help="List of tokenized data paths to load from. If using chat data, provide a single path.",
)
parser.add_argument("--split", type=str, default="99,1,0", help="Train,Val,Test ratios to split data")
parser.add_argument("--index_mapping_dir", type=str, help="Folder to write cached data indices")
parser.add_argument("--use-chat-data", action="store_true", help="Use chat data for fine-tuning.")
parser.add_argument(
"--chat-template-path",
type=str,
help="Path to Chat template .txt file to use for chat data. Only provide if overriding default chat template in HuggingFace tokenizer.",
)
parser.add_argument(
"--use_mock_data", action="store_true", help="Use mock data instead of custom data in --data_paths"
)
parser.add_argument("--seq_length", type=int, required=True, help="Number of tokens per input sample")
parser.add_argument(
"--tokenizer",
type=str,
help="Name of tokenizer model to override default. Required if using chat data (--use-chat-data).",
)
parser.add_argument("--lr", type=float, default=1e-4, help="Base LR for Cosine-Annealing scheduler")
parser.add_argument("--min_lr", type=float, default=1e-5, help="Minimum LR for Cosine-Annealing scheduler")
parser.add_argument("--warmup_steps", type=int, default=50, help="Number of scheduler warmup steps")
parser.add_argument("--val_check_interval", type=int, default=100, help="Validate + checkpoint every _ steps")
parser.add_argument("--limit_val_batches", type=int, default=32, help="Number of batches per validation stage")
parser.add_argument("--log_interval", type=int, default=10, help="Write to log every _ steps")
parser.add_argument("--legacy_ckpt", action="store_true", help="Load ckpt saved with TE < 1.14")
return parser.parse_args()
def _read_chat_template(template_path: str):
# pylint: disable=C0116
if not template_path:
return None
with open(template_path, 'r') as f:
return f.read().strip()
if __name__ == "__main__":
args = get_args()
## Initialize the strategy and trainer
strategy = nl.MegatronStrategy(
tensor_model_parallel_size=args.tp_size,
pipeline_model_parallel_size=args.pp_size,
context_parallel_size=args.cp_size,
expert_model_parallel_size=args.ep_size,
sequence_parallel=(args.tp_size > 1),
ddp=DistributedDataParallelConfig(
grad_reduce_in_fp32=True,
overlap_grad_reduce=True,
overlap_param_gather=True,
check_for_nan_in_grad=True,
average_in_collective=True,
),
ckpt_load_strictness=StrictHandling.LOG_ALL if args.legacy_ckpt else None,
)
trainer = nl.Trainer(
devices=args.devices,
num_nodes=args.num_nodes,
max_steps=args.max_steps,
log_every_n_steps=args.log_interval,
val_check_interval=args.val_check_interval,
limit_val_batches=args.limit_val_batches,
strategy=strategy,
accelerator="gpu",
plugins=nl.MegatronMixedPrecision(
precision=args.precision,
params_dtype=torch.bfloat16 if "bf16" in args.precision else torch.float32,
autocast_enabled=False,
grad_reduce_in_fp32=True,
),
)
## Set up dataset
if not args.use_mock_data and not args.data_paths:
raise ValueError("Must provide either custom dataset(s) in --data_paths or set --use_mock_data.")
if args.use_mock_data:
logging.warning("Using Mock Data for training!")
data = MockDataModule(seq_length=args.seq_length, global_batch_size=args.gbs, micro_batch_size=args.mbs)
elif args.use_chat_data:
assert len(args.data_paths) == 1, "If using chat data, provide a single path."
assert args.tokenizer is not None, "Tokenizer is required if using chat data."
chat_template = _read_chat_template(args.chat_template_path)
tokenizer = get_tokenizer(args.tokenizer, chat_template=chat_template)
if '{% generation %}' not in tokenizer.tokenizer.chat_template:
if not args.chat_template_path:
raise ValueError(
"Tokenizer does not contain the '{% generation %}' keyword. Please provide a chat template path using --chat-template-path."
)
raise ValueError(
"Please ensure the chat template includes a '{% generation %}' keyword for proper assistant mask during training. See https://github.com/huggingface/transformers/pull/30650"
)
data = ChatDataModule(
dataset_root=args.data_paths[0],
seq_length=args.seq_length,
tokenizer=tokenizer,
global_batch_size=args.gbs,
micro_batch_size=args.mbs,
use_hf_tokenizer_chat_template=True,
)
else:
data = llm.PreTrainingDataModule(
paths=args.data_paths,
seq_length=args.seq_length,
global_batch_size=args.gbs,
micro_batch_size=args.mbs,
split=args.split,
index_mapping_dir=args.index_mapping_dir,
)
## Set up optimizer
optim_config = OptimizerConfig(
optimizer="adam",
lr=args.lr,
bf16=("bf16" in args.precision),
use_distributed_optimizer=True,
)
sched = CosineAnnealingScheduler(
max_steps=args.max_steps,
warmup_steps=args.warmup_steps,
constant_steps=0,
min_lr=args.min_lr,
)
optim = nl.MegatronOptimizerModule(optim_config, sched)
## Set up checkpointing and logging
checkpoint_callback = ModelCheckpoint(
monitor="val_loss",
save_top_k=1,
every_n_train_steps=args.val_check_interval,
)
logger = nl.NeMoLogger(
name=args.name,
log_dir=args.log_dir,
ckpt=checkpoint_callback,
tensorboard=TensorBoardLogger(os.path.join(args.log_dir, args.name)),
update_logger_directory=False,
)
## Set up resume and/or restore functionality
resume = nl.AutoResume(
resume_if_exists=True,
resume_ignore_no_checkpoint=True,
restore_config=nl.RestoreConfig(path=args.model_path),
)
if args.teacher_path:
llm.distill(
student_model_path=args.model_path,
teacher_model_path=args.teacher_path,
distillation_config_path=args.kd_config,
data=data,
trainer=trainer,
log=logger,
resume=resume,
optim=optim,
tokenizer=get_tokenizer(args.tokenizer) if args.tokenizer else None,
)
else:
llm.train(
model=args.model_path,
data=data,
trainer=trainer,
optim=optim,
log=logger,
resume=resume,
tokenizer="data" if args.use_chat_data else "model",
)
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